courseraАнализ данных

Regression and Classification

Курс от University of Colorado Boulder

Средний≈ 34.8 чАнглийский

О курсе

Introduction to Statistical Learning will explore concepts in statistical modeling, such as when to use certain models, how to tune those models, and if other options will provide certain trade-offs. We will cover Regression, Classification, Trees, Resampling, Unsupervised techniques, and much more! This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

Навыки, которые вы освоите

Classification Algorithms Statistical Modeling Regression Analysis R Programming Logistic Regression Statistical Machine Learning Model Evaluation Model Training Statistical Analysis Predictive Modeling Machine Learning Machine Learning Algorithms Supervised Learning Data Science Classification And Regression Tree (CART)Statistical Inference Statistical Methods Unsupervised Learning

Программа курса

6 модулей · 58 учебных материалов

01Statistical Learning Introduction13 материалов

Introduction

Course Updates and Accessibility SupportЧтение Earn Academic Credit for your Work!Чтение Course SupportЧтение Introduce YourselfОбсуждение

Setting the Foundation

Учитесь у экспертов

James Bird

Instructor

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Инвестируйте в себя

Новые знания — в удобное для вас время.

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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 34.8 ч

6 модулей

Язык: Английский

Субтитры: Арабский, Французский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Нидерландский, Корейский, Немецкий, Русский, Тайский, Индонезийский, Шведский, Турецкий, Испанский, Хинди, Японский, Казахский, Польский

Regression and Classification

О курсе

Навыки, которые вы освоите

Программа курса

Introduction

Setting the Foundation

Учитесь у экспертов

James Bird

General Concepts

Accuracy

Bayes Classifier

Assessments

Coefficients

Concepts

Multiple Linear Regression

Predictors

Model Specifics

Assessments

Logistic Regression

Logistic Regression Part II

Generative Models

LDA

QDA & Naive Bayes

Poisson Regression, Link Functions & Conclusion

Assessments